Summary of Learning From Uncertain Data: From Possible Worlds to Possible Models, by Jiongli Zhu et al.
Learning from Uncertain Data: From Possible Worlds to Possible Models
by Jiongli Zhu, Su Feng, Boris Glavic, Babak Salimi
First submitted to arxiv on: 28 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Databases (cs.DB); Symbolic Computation (cs.SC)
GrooveSquid.com Paper Summaries
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an efficient method for learning linear models from uncertain data. The uncertainty is represented as a set of possible variations in the data, leading to predictive multiplicity. The authors leverage abstract interpretation and zonotopes to compactly represent these dataset variations, enabling the symbolic execution of gradient descent on all possible worlds simultaneously. They develop techniques to ensure that this process converges to a fixed point and derive closed-form solutions for this fixed point. The method provides sound over-approximations of all possible optimal models and viable prediction ranges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers learn from data that might be slightly different each time it’s used. This is important because sometimes the data itself can be uncertain or have small variations, which can affect how well a model performs. The authors developed a new way to handle these variations by using something called abstract interpretation and zonotopes. This allows them to figure out what the best possible models are and what range of predictions they might make. |
Keywords
» Artificial intelligence » Gradient descent
